User equipment, base station, channel estimation and feedback system of user equipment and base station

Abstract

A user equipment in wireless communication, a base station, a joint training device of user equipment and base station, a joint channel estimation and feedback system of user equipment and base station, a feedback channel state information generation method executed by user equipment, a channel matrix generation method executed by base station, a joint training method of user equipment and base station, and a joint channel estimation and feedback method for user equipment and base station. By generating feedback channel state information by user equipment according to actual pilot signals, introducing a deeper residual learning neural network in the base station to reconstruct the channel matrix of the base station according to the feedback channel state information. Even if the actual received pilot signal is a non-complete low-resolution part, the base station can reconstruct a complete high-resolution channel matrix.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication, and specifically to a user equipment, a base station, a joint training device for the user equipment and the base station, a joint channel estimation and feedback system for the user equipment and the base station, a method for generating feedback channel state information performed by the user equipment, a method for generating a channel matrix performed by the base station, a joint training method for the user equipment and the base station, and a joint channel estimation and feedback method for the user equipment and the base station. Background Technology

[0002] Massive Multiple-Input Multiple-Output (MIMO) systems are one of the key technologies in 5G wireless communication. This technology significantly increases the throughput of wireless communication systems by configuring a large number of antennas at the base station to form multiple independent channels in the spatial domain. MIMO systems require the base station to have accurate channel state information, which is then used for precoding to eliminate interference between multiple users. One common method for channel state acquisition is for users to measure downlink channel state information and feed it back to the base station. However, considering the large number of antennas used at the base station, feeding back complete channel state information would result in significant resource overhead.

[0003] Therefore, there is a need for a channel estimation and feedback method capable of high-compression-rate channel state information (CSO) and fast, accurate reconstruction of CSO from the highly compressed feedback information. Under the assumption that the user equipment (UE) receives ideal, complete CSO, the UE generates a feedback signal using this complete CSO, and the base station then uses this feedback signal to reconstruct the ideal, complete channel matrix. However, in practical MIMO systems, the actual pilot signals received by the UE are typically incomplete, low-resolution components. If the UE performs channel estimation and feedback based on these low-resolution pilot signals, the base station will find it difficult to reconstruct the complete channel matrix from the feedback signal. Summary of the Invention

[0004] This disclosure is made in view of the above-mentioned problems. This disclosure provides a user equipment, a base station, a joint training apparatus for user equipment and base station in wireless communication, a joint channel estimation and feedback system for user equipment and base station, a method for generating feedback channel state information performed by user equipment, a method for generating channel matrix performed by base station, a joint training method for user equipment and base station, and a joint channel estimation and feedback method for user equipment and base station.

[0005] According to one aspect of this disclosure, a user equipment is provided, comprising: a receiving unit for receiving downlink transmission data including pilot signals from a base station; an encoding unit for encoding the pilot signals into feedback channel state information; and a transmitting unit for transmitting the feedback channel state information to the base station, for the base station to reconstruct the channel matrix of the base station based on the feedback channel state information.

[0006] According to one aspect of the present disclosure, the user equipment wherein the pilot signal is a pilot signal with a frequency controlled by the base station.

[0007] According to one aspect of the present disclosure, in a user equipment, the coding unit is configured with a coding neural network, the coding neural network including at least one fully connected layer for quantizing and compressing the pilot signal into a one-dimensional vector as the feedback channel state information.

[0008] According to another aspect of this disclosure, a base station is provided, comprising: a transmitting unit for transmitting downlink transmission data including pilot signals to a user equipment; a receiving unit for receiving uplink transmission data from the user equipment, the uplink transmission data including feedback channel state information generated based on the pilot signals; and a decoding unit for decoding the feedback channel state information to obtain the channel matrix of the base station.

[0009] According to another aspect of this disclosure, in a base station, the transmitting unit controls the frequency of the pilot signal.

[0010] According to another aspect of this disclosure, in a base station, the decoding unit is configured with a decoding neural network, the decoding neural network comprising at least a multi-layer residual convolutional neural network, for super-resolution reconstruction of the feedback channel state information into the channel matrix of the base station.

[0011] According to another aspect of this disclosure, a joint training apparatus for a user equipment and a base station is provided, comprising: a receiving unit for receiving pilot signals and training pilot signals from the base station; a training unit for encoding the pilot signals into feedback channel state information using at least an encoding neural network, and decoding the feedback channel state information using at least a decoding neural network to reconstruct the channel matrix of the base station; obtaining a training channel matrix based on the training pilot signals, and the training unit constructing a loss function based on the channel matrix and the training channel matrix, jointly training the encoding neural network and the decoding neural network; and outputting the parameters of the encoding neural network and the decoding neural network.

[0012] According to another aspect of this disclosure, a joint channel estimation and feedback system including a user equipment and a base station is provided, comprising: a user equipment configured to receive downlink transmission data including pilot signals from a base station, encode the pilot signals into feedback channel state information, and transmit the feedback channel state information to the base station; and a base station configured to transmit downlink transmission data including pilot signals to the user equipment, receive uplink transmission data from the user equipment, the uplink transmission data including feedback channel state information generated based on the pilot signals; and decode the feedback channel state information to obtain the channel matrix of the base station.

[0013] According to another aspect of this disclosure, a method for generating feedback channel state information executed by a user equipment is provided, comprising: receiving downlink transmission data including pilot signals from a base station; encoding the pilot signals into feedback channel state information; and sending the feedback channel state information to the base station for the base station to reconstruct the channel matrix of the base station based on the feedback channel state information.

[0014] According to another aspect of this disclosure, a channel matrix generation method performed by a base station is provided, comprising: sending downlink transmission data including pilot signals to a user equipment; receiving uplink transmission data from the user equipment, the uplink transmission data including feedback channel state information generated based on the pilot signals; and decoding the feedback channel state information to obtain the channel matrix of the base station.

[0015] According to another aspect of this disclosure, a joint training method for a user equipment and a base station is provided, comprising: receiving pilot signals and training pilot signals from the base station; encoding the pilot signals into feedback channel state information using at least an encoding neural network, decoding the feedback channel state information using at least a decoding neural network to reconstruct the channel matrix of the base station, obtaining a training channel matrix based on the training pilot signals, constructing a loss function based on the channel matrix and the training channel matrix, jointly training the encoding neural network and the decoding neural network; and outputting the parameters of the encoding neural network and the decoding neural network.

[0016] According to another aspect of this disclosure, a joint channel estimation and feedback method for a user equipment and a base station is provided, comprising: the base station transmitting downlink transmission data including pilot signals to the user equipment; the user equipment encoding the pilot signals into feedback channel state information and transmitting the feedback channel state information to the base station; and the base station receiving uplink transmission data from the user equipment, the uplink transmission data including the feedback channel state information generated based on the pilot signals; and the base station decoding the feedback channel state information to obtain the base station's channel matrix.

[0017] As will be described in detail below, according to the user equipment, base station, joint training apparatus for user equipment and base station, joint channel estimation and feedback system for user equipment and base station in wireless communication disclosed herein, method for generating feedback channel state information performed by user equipment, method for generating channel matrix performed by base station, joint training method for user equipment and base station, and joint channel estimation and feedback method for user equipment and base station, feedback channel state information is generated by user equipment based on actual pilot signals, and a deeper residual learning neural network is introduced in base station to reconstruct the base station's channel matrix based on the feedback channel state information. This achieves the ability for the base station to reconstruct a complete high-resolution channel matrix even when the actually received pilot signal is an incomplete, low-resolution portion.

[0018] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0019] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 This is a schematic diagram outlining an application scenario of a wireless communication system according to embodiments of the present disclosure;

[0021] Figure 2 This is a block diagram illustrating a user equipment according to an embodiment of the present disclosure;

[0022] Figure 3A and 3B This is a schematic diagram illustrating pilot signals according to an embodiment of the present disclosure;

[0023] Figure 4 This is a flowchart illustrating a method for generating feedback channel state information performed by a user equipment according to an embodiment of the present disclosure;

[0024] Figure 5 This is a block diagram illustrating a base station according to an embodiment of the present disclosure;

[0025] Figure 6 This is a flowchart illustrating a channel matrix generation method performed by a base station according to an embodiment of the present disclosure;

[0026] Figure 7 This is a block diagram illustrating a joint channel estimation and feedback system according to an embodiment of the present disclosure;

[0027] Figure 8 This is a flowchart illustrating a joint channel estimation and feedback method for user equipment and base station according to an embodiment of the present disclosure;

[0028] Figure 9 This is a block diagram illustrating a training apparatus and its training joint channel estimation and feedback system according to an embodiment of the present disclosure;

[0029] Figure 10 This is a flowchart illustrating a joint training method for user equipment and a base station according to an embodiment of the present disclosure; and

[0030] Figure 11 This is a schematic diagram of the hardware structure of the device involved in the embodiments of this disclosure. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0032] Figure 1 This is a schematic diagram of a wireless communication system in which embodiments of the present disclosure can be applied. The wireless communication system can be a 5G system or any other type of wireless communication system, such as a Long Term Evolution (LTE) system or an LTE-A (advanced) system.

[0033] like Figure 1 As shown, the wireless communication system may include a base station 10 and a user equipment 20, with base station 10 serving as the base station for user equipment 20. Base station 10 can transmit signals to user equipment 20, and correspondingly, user equipment 20 can receive signals from base station 10. Furthermore, user equipment 20 can transmit signals to base station 10 (e.g., feedback), and correspondingly, base station 10 can receive signals from user equipment 20. User equipment 20 may be configured with an artificial intelligence-enabled signal processor (e.g., a signal encoder) to process signals transmitted to base station 10 using artificial intelligence. Correspondingly, base station 10 may be configured with an artificial intelligence-enabled signal processor (e.g., a signal decoder) corresponding to user equipment 20 to process signals received from user equipment 20 using artificial intelligence.

[0034] It needs to be recognized that, despite Figure 1Only one base station and one user equipment are shown in the illustration, but this is merely illustrative; the wireless communication system may include multiple base stations and / or multiple user equipment. Accordingly, the wireless communication system may include multiple cells. Furthermore, in this disclosure, cells and base stations are sometimes used interchangeably.

[0035] like Figure 1 As shown, base station 10 can transmit downlink transmission data to user equipment 20 on the downlink channel. As will be described in detail below, in embodiments of this disclosure, the downlink transmission data may include a reference signal, such as pilot signal 11. Based on the pilot signal 11, user equipment 20 transmits feedback channel state information 21 to base station 10 on the uplink channel. Base station 10 will reconstruct the current channel matrix based on the feedback channel state information 21 fed back by user equipment 20, in order to optimize the configuration of the downlink channel.

[0036] It is important to note that the "reference signal" here can be, for example, a reference signal (RS) on the downlink control channel, service data on the downlink data channel, and / or a demodulation reference signal (DMRS). When the base station has RS configured and RS configuration is available, the base station can transmit RS on the downlink control channel. The downlink control channel here can be, for example, a Physical Downlink Control Channel (PDCCH), a Physical Broadcast Channel (PBCH), or a Physical Control Format Indicator Channel (PCFICH), etc. The reference signal here can be one or more of the following: Channel State Information Reference Signal (CSI-RS), Primary Synchronization Signal (PSS) / Secondary Synchronization Signal (SSS), DMRS, or Synchronized Signal Block (SSB), etc. Feedback channel state information can be one or more of the following: Channel State Information (CSI), Reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), or Synchronization Block Index (SSB-index). Taking CSI as an example, CSI can include one or more of the following: Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), Rank Indication (RI), Channel Direction Information (CDI), Channel Feature Vector, or CSI-RS Indicator (CRI).

[0037] The following will describe in further detail the base station and user equipment according to embodiments of the present disclosure, and the joint channel estimation and feedback system implemented thereon.

[0038] Figure 2 This is a block diagram illustrating a user equipment according to an embodiment of the present disclosure. Figure 2 As shown, the user equipment 20 according to an embodiment of the present disclosure includes a receiving unit 201, an encoding unit 202, and a transmitting unit 203.

[0039] The receiving unit 201 is used to receive downlink transmission data, including pilot signal 200, from the base station. Figure 3 is a schematic diagram illustrating the pilot signal according to an embodiment of the present disclosure. Figure 3A As shown, in a potentially fast-fading environment, pilot symbols are inserted at equal intervals at specific subcarrier positions in the frequency domain, ensuring that pilots are present on specific subcarriers within an OFDM symbol, thus enabling timely tracking of channel changes. In other words, pilot signal 200 is a pilot signal whose frequency is controlled by base station 10.

[0040] It is readily understood that the pilot signals according to embodiments of this disclosure are not limited to... Figure 3A The comb-shaped pilot signal is shown. Figure 3B Another example pilot signal according to an embodiment of this disclosure is shown. For example... Figure 3B As shown, pilot signals are transmitted in specific NR RS ports (ports 0-15, ports 16-32) according to predetermined transmission and multiplexing methods.

[0041] Encoding unit 202 is used to encode the pilot signal 200 into feedback channel state information 204. In actual wireless communication systems, the pilot signal 200 received by receiving unit 201 is usually a low-resolution portion of the entire reference signal. Since the pilot signal 200 is an incomplete reference signal, the feedback channel state information 204 generated by encoding unit 202 will also be incomplete channel state information (CSI).

[0042] In embodiments of this disclosure, the encoding unit 202 is configured with an encoding neural network 2020, which includes at least one fully connected layer for quantizing and compressing the pilot signal 200 into a one-dimensional vector as the feedback channel state information. By configuring only one fully connected layer, the processing complexity of the user equipment is reduced. In addition to the fully connected layer, the encoding neural network 2020 may also include other convolutional layers for performing quantization, compression, encoding, and modulation processes on the pilot signal 200.

[0043] The transmitting unit 203 is used to transmit the feedback channel state information 204 to the base station 10, so that the base station 10 can reconstruct the channel matrix of the base station based on the feedback channel state information 204. As will be described in detail below, according to an embodiment of the present disclosure, the base station 10 uses a super-resolution network to recover and reconstruct a complete channel matrix based on the incomplete feedback channel state information 204.

[0044] Figure 4 This is a flowchart illustrating a method for generating feedback channel state information performed by a user equipment according to an embodiment of the present disclosure. Figure 4 As shown, the feedback channel state information generation method performed by the user equipment according to an embodiment of this disclosure includes the following steps.

[0045] In step S401, downlink transmission data, including pilot signals, is received from the base station. Afterward, the process proceeds to step S402.

[0046] In step S402, the pilot signal is encoded into feedback channel state information. Afterward, the process proceeds to step S403.

[0047] In step S403, the feedback channel state information is sent to the base station, so that the base station can reconstruct the channel matrix of the base station based on the feedback channel state information.

[0048] Figure 5 This is a block diagram illustrating a base station implemented according to this disclosure. For example... Figure 5 As shown, the base station 10 according to an embodiment of the present disclosure includes a transmitting unit 101, a receiving unit 102, and a decoding unit 103.

[0049] The transmitting unit 101 is used to transmit downlink transmission data, including pilot signal 200, to the user equipment 20. Pilot signal 200 is a pilot signal whose frequency is controlled by the base station 10. For example, in a potentially fast fading environment, pilot symbols are inserted at equal intervals at specific subcarrier positions in the frequency domain, so that there are pilots on specific subcarriers within an OFDM symbol, thereby enabling timely tracking of channel changes.

[0050] The receiving unit 102 is used to receive uplink transmission data from the user equipment 20, the uplink transmission data including feedback channel state information 204 generated based on the pilot signal 200. (Refer to the above) Figure 2 and Figure 4 The user equipment 20 encodes the pilot signal 200, which is an incomplete reference signal, into feedback channel state information 204, which is an incomplete channel state information (CSI).

[0051] Decoding unit 103 is used to decode the feedback channel state information 204 to obtain the channel matrix 205 of the base station. Decoding unit 103 is configured with a decoding neural network 1030, which includes at least a multi-layer residual convolutional neural network for super-resolution reconstruction of the feedback channel state information 204 into the channel matrix 205 of the base station 10. For example, the decoding neural network 1030 includes one fully connected layer, one reconstruction layer, and a multi-layer residual convolutional neural network. The multi-layer residual convolutional neural network is, for example, a 16-layer multi-layer residual convolutional neural network. The base station 10 reconstructs the complete channel matrix through super-resolution reconstruction using the multi-layer residual convolutional neural network.

[0052] Figure 6 This is a flowchart illustrating a channel matrix generation method performed by a base station according to an embodiment of the present disclosure. Figure 6 As shown, the channel matrix generation method performed by the base station according to an embodiment of this disclosure includes the following steps.

[0053] In step S601, downlink transmission data, including pilot signals, is sent to the user equipment. Afterward, the process proceeds to step S602.

[0054] In step S602, uplink transmission data is received from the user equipment, the uplink transmission data including feedback channel state information generated based on the pilot signal. Afterwards, the process proceeds to step S603.

[0055] In step S603, the feedback channel state information is decoded to obtain the channel matrix of the base station.

[0056] The base station and user equipment according to embodiments of the present disclosure have been described above. The joint channel estimation and feedback system and the joint channel estimation and feedback method for user equipment and base station according to embodiments of the present disclosure will be further described below. Figure 7 This is a block diagram illustrating a joint channel estimation and feedback system according to an embodiment of the present disclosure; Figure 8 This is a flowchart illustrating a joint channel estimation and feedback method for user equipment and base station according to an embodiment of the present disclosure.

[0057] like Figure 7 As shown, the joint channel estimation and feedback system 70 according to an embodiment of this disclosure includes a base station 10 and a user equipment 20. The base station 10 and user equipment 20 are as described above. Figure 2 and Figure 5 The base station 10 according to an embodiment of the present disclosure includes a transmitting unit 101, a receiving unit 102, and a decoding unit 103. The user equipment 20 according to an embodiment of the present disclosure includes a receiving unit 201, an encoding unit 202, and a transmitting unit 203.

[0058] The transmitting unit 101 of the base station 10 transmits downlink transmission data, including pilot signal 200, to the user equipment 20. Pilot signal 200 is a pilot signal whose frequency is controlled by the base station 10.

[0059] The receiving unit 201 of the user equipment 20 is used to receive downlink transmission data, including pilot signal 200, from the base station.

[0060] The encoding unit 202 of the user equipment 20 encodes the pilot signal 200 into feedback channel state information 204. In actual wireless communication systems, the pilot signal 200 received by the receiving unit 201 is usually a low-resolution portion of the entire reference signal. Since the pilot signal 200 is an incomplete reference signal, the feedback channel state information 204 generated by the encoding unit 202 will also be incomplete channel state information (CSI).

[0061] The transmitting unit 203 of the user equipment 20 sends the feedback channel state information 204 to the base station 10, so that the base station 10 can reconstruct the channel matrix of the base station based on the feedback channel state information 204.

[0062] The receiving unit 102 of the base station 10 receives uplink transmission data from the user equipment 20, the uplink transmission data including feedback channel state information 204 generated based on the pilot signal 200.

[0063] The decoding unit 103 of base station 10 decodes the feedback channel state information 204 to obtain the channel matrix 205 of the base station. The decoding unit 103 is configured with a decoding neural network 1030, which includes at least a multi-layer residual convolutional neural network for super-resolution reconstruction of the feedback channel state information 204 into the channel matrix 205 of base station 10. For example, the decoding neural network 1030 includes one fully connected layer, one reconstruction layer, and a multi-layer residual convolutional neural network. The multi-layer residual convolutional neural network may be, for example, a 16-layer multi-layer residual convolutional neural network. Base station 10 reconstructs the complete channel matrix using the multi-layer residual convolutional neural network.

[0064] like Figure 8 As shown, the joint channel estimation and feedback method for user equipment and base station according to embodiments of this disclosure includes the following steps.

[0065] In step S801, the base station sends downlink transmission data, including pilot signals, to the user equipment. Afterward, the process proceeds to step S802.

[0066] In step S802, the user equipment encodes the pilot signal into feedback channel state information and sends the feedback channel state information to the base station. Afterward, the process proceeds to step S803.

[0067] In step S803, the base station receives uplink transmission data from the user equipment, the uplink transmission data including the feedback channel state information generated based on the pilot signal. Afterwards, the process proceeds to step S804.

[0068] In step S804, the base station decodes the feedback channel state information to obtain the base station's channel matrix.

[0069] In the joint channel estimation and feedback system 70 described above, a decoding neural network and an encoding neural network are respectively configured in the base station 10 and user equipment 20. To configure the decoding neural network and the encoding neural network, joint network training needs to be performed on the base station 10 and user equipment 20 of the joint channel estimation and feedback system 70. The joint training apparatus and joint training method used to perform the joint network training will be further described below.

[0070] Figure 9 This is a block diagram illustrating a training apparatus and its training joint channel estimation and feedback system according to an embodiment of the present disclosure. Figure 9 As shown, the training device 90 includes a receiving unit 901 and a training unit 903.

[0071] The receiving unit 901 is used to receive the pilot signal 91 and the training pilot signal 92 from the base station 10 in the joint channel estimation and feedback system 70. As mentioned above, the pilot signal 91 is typically a low-resolution portion of the entire reference signal, i.e., an incomplete reference signal. The training pilot signal 92 is a high-resolution complete reference signal.

[0072] The training unit 903 is used to encode the pilot signal into feedback channel state information using at least an encoding neural network, and to decode the feedback channel state information using at least a decoding neural network, so as to reconstruct the channel matrix 93 of the base station.

[0073] Training unit 903 acquires training channel matrix 94 based on the training pilot signal 92, and constructs a loss function based on channel matrix 93 and training channel matrix 94, jointly training the encoding neural network and the decoding neural network. That is, the training channel matrix 94 acquired based on the training pilot signal 92 is a complete channel matrix, and the reconstructed channel matrix 93 needs to be sufficiently close to the training channel matrix 94. The training process can end when the difference between channel matrix 93 and training channel matrix 94 meets a predetermined condition. The trained encoding neural network can encode and compress incomplete low-resolution portions of the reference signal, and the decoding neural network can reconstruct the complete channel matrix using super-resolution.

[0074] The training unit 903 further outputs the parameters of the trained encoding neural network and the decoding neural network. The parameters of the encoding neural network and the decoding neural network can then be further deployed to the user equipment and the base station, respectively.

[0075] Figure 10 This is a flowchart illustrating a joint training method for user equipment and a base station according to an embodiment of the present disclosure. The joint training method for user equipment and a base station according to an embodiment of the present disclosure includes the following steps.

[0076] In step S1001, pilot signals and training pilot signals are received from the base station. Afterward, the process proceeds to step S1002.

[0077] In step S1002, the pilot signal is encoded into feedback channel state information using at least an encoding neural network, and the feedback channel state information is decoded using at least a decoding neural network to reconstruct the channel matrix of the base station. Afterward, the process proceeds to step S1003.

[0078] In step S1003, the training channel matrix is ​​obtained based on the training pilot signal. Afterwards, the process proceeds to step S1004.

[0079] In step S1004, a loss function is constructed based on the channel matrix and the training channel matrix, and the encoding neural network and the decoding neural network are jointly trained. Afterward, the process proceeds to step S1005.

[0080] In step S1005, the parameters of the trained encoding neural network and decoding neural network are output. These parameters can then be further deployed to the user equipment and the base station, respectively.

[0081] According to the user equipment, base station, joint training device for user equipment and base station, joint channel estimation and feedback system for user equipment and base station in wireless communication disclosed herein, method for generating feedback channel state information executed by user equipment, method for generating channel matrix executed by base station, joint training method for user equipment and base station, and joint channel estimation and feedback method for user equipment and base station, feedback channel state information is generated by user equipment based on actual pilot signals, and a deeper residual learning neural network is introduced in base station to reconstruct the base station's channel matrix based on the feedback channel state information. This achieves the ability for base station to reconstruct a complete high-resolution channel matrix even when the actually received pilot signal is an incomplete, low-resolution portion.

[0082] <Hardware Structure>

[0083] Furthermore, the block diagrams used in the above description of the embodiments illustrate blocks based on function. These functional blocks (structural units) are implemented through any combination of hardware and / or software. Moreover, the means of implementing each functional block are not particularly limited. That is, each functional block can be implemented using a single device that is physically and / or logically combined, or it can be implemented using multiple devices by directly and / or indirectly (e.g., via wired and / or wireless) connecting two or more physically and / or logically separate devices.

[0084] For example, an apparatus of one embodiment of this disclosure (such as a first communication device, a second communication device, or a flight user terminal) can function as a computer performing the wireless communication method of this disclosure. Figure 11 This is a schematic diagram of the hardware structure of the device 1100 (base station or user equipment) according to an embodiment of the present disclosure. The device 1100 (base station or user equipment) described above can be configured as a computer device that physically includes a processor 1110, a memory 1120, a storage device 1130, a communication device 1140, an input device 1150, an output device 1160, a bus 1170, etc.

[0085] Additionally, in the following description, the word "device" can be replaced with circuit, equipment, unit, etc. The hardware structure of user equipment and base stations may include one or more of the devices shown in the figures, or may not include some of the devices.

[0086] For example, only one processor 1110 is shown, but there can be multiple processors. Furthermore, processing can be performed by one processor, or by more than one processor simultaneously, sequentially, or using other methods. Additionally, processor 1110 can be mounted on more than one chip.

[0087] The functions of device 1100 are implemented, for example, by reading the specified software (program) into hardware such as processor 1110 and memory 1120, so that processor 1110 can perform calculations, control the communication performed by communication device 1140, and control the reading and / or writing of data in memory 1120 and storage 1130.

[0088] Processor 1110, for example, enables the operating system to operate, thereby controlling the computer as a whole. Processor 810 may be composed of a central processing unit (CPU) including interfaces with peripheral devices, control devices, arithmetic devices, registers, etc.

[0089] Furthermore, the processor 1110 reads programs (program code), software modules, data, etc., from the memory 1130 and / or communication device 1140 into the memory 1120, and performs various processes accordingly. As a program, a program that causes the computer to perform at least a portion of the actions described in the above embodiments may be employed.

[0090] Memory 1120 is a computer-readable recording medium, and may be constituted by at least one of the following: Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Programmable ROM (EEPROM), Random Access Memory (RAM), or other suitable storage media. Memory 1120 may also be referred to as a register, cache, main memory (main storage device), etc. Memory 1120 may store executable programs (program code), software modules, etc., for implementing the methods involved in one embodiment of this disclosure.

[0091] The memory 1130 is a computer-readable recording medium, and may be constituted by at least one of the following: a flexible disk, a floppy disk, a magneto-optical disk (e.g., a CD-ROM (Compact Disc ROM), a Digital Universal Optical Disc, a Blu-ray disc), a removable disk, a hard disk, a smart card, a flash memory device (e.g., a card, a stick, a key driver), a magnetic stripe, a database, a server, or other suitable storage media. The memory 1130 may also be referred to as an auxiliary storage device.

[0092] Communication device 1140 is hardware (transmitting and receiving device) used for communication between computers via wired and / or wireless networks, and is also referred to as a network device, network controller, network interface card (NIC), communication module, etc. To implement, for example, frequency division duplex (FDD) and / or time division duplex (TDD), communication device 1140 may include high-frequency switches, duplexers, filters, frequency synthesizers, etc. For example, the aforementioned transmitting unit and receiving unit can be implemented using communication device 1140.

[0093] Input device 1150 is an input device that accepts input from external sources (e.g., keyboard, mouse, microphone, switch, button, sensor, etc.). Output device 1160 is an output device that performs output to external sources (e.g., display, speaker, light-emitting diode (LED) lamp, etc.). Alternatively, input device 1150 and output device 1160 can also be integrated into a single structure (e.g., a touch panel).

[0094] Furthermore, the processor 1110, memory 1120, and other devices are connected via a bus 1170 for communication of information. The bus 1170 can consist of a single bus or different buses between devices.

[0095] Furthermore, base stations and user equipment may include hardware such as microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable logic devices (PLDs), and field-programmable gate arrays (FPGAs), which can be used to implement some or all of the functional blocks. For example, processor 1110 can be installed using at least one of these hardware components.

[0096] (Modified Example)

[0097] Furthermore, the terms used in this specification and / or those necessary for understanding this specification may be used interchangeably with terms that have the same or similar meanings. For example, a channel and / or symbol may also be a signal (signaling). Additionally, a signal may also be a message. A reference signal may also be simply referred to as RS (Reference Signal), and depending on the applicable standard, may also be called a pilot, pilot signal, etc. Furthermore, a component carrier (CC) may also be referred to as a cell, frequency carrier, carrier frequency, etc.

[0098] Furthermore, the information and parameters described in this specification can be expressed in absolute values, relative values ​​to specified values, or other corresponding information. For example, wireless resources can be indicated by a specified index. Moreover, the formulas used to apply these parameters may differ from those explicitly disclosed in this specification.

[0099] The names used for parameters, etc., in this specification are not limiting in any way. For example, various channels (Physical Uplink Control Channel (PUCCH), Physical Downlink Control Channel (PDCCH), etc.) and information elements can be identified by any appropriate name, and therefore the various names assigned to these various channels and information elements are not limiting in any way.

[0100] The information, signals, etc., described in this specification can be represented using any of a wide variety of different technologies. For example, data, commands, instructions, information, signals, bits, symbols, chips, etc., that may be mentioned in all of the above descriptions can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any combination thereof.

[0101] Furthermore, information and signals can be output from upper layers to lower layers and / or from lower layers to upper layers. Information and signals can be input or output via multiple network nodes.

[0102] Input or output information and signals can be stored in a specific location (such as memory) or managed through a management table. Input or output information and signals can be overwritten, updated, or supplemented. Output information and signals can be deleted. Input information and signals can be sent to other devices.

[0103] The notification of information is not limited to the methods / implementations described in this specification, and may also be carried out by other methods. For example, the notification of information may be implemented by physical layer signaling (e.g., downlink control information (DCI), uplink control information (UCI)), upper layer signaling (e.g., radio resource control (RRC) signaling, broadcast information (master information block (MIB), system information block (SIB) etc.), media access control (MAC) signaling), other signals, or combinations thereof.

[0104] In addition, physical layer signaling can also be referred to as L1 / L2 (Layer 1 / Layer 2) control information (L1 / L2 control signals), L1 control information (L1 control signals), etc. Furthermore, RRC signaling can also be referred to as RRC messages, such as RRC connection setup messages, RRC connection reconfiguration messages, etc. Additionally, MAC signaling can be communicated, for example, through a MAC control unit (MAC CE (Control Element)).

[0105] Furthermore, notification of specified information (e.g., notification of “for X”) is not limited to being made explicitly, but can also be made implicitly (e.g., by not making notification of the specified information, or by notifying other information).

[0106] The determination can be made by a value represented by 1 bit (0 or 1), by a true or false Boolean value, or by a numerical comparison (e.g., a comparison with a specified value).

[0107] Whether it is called software, firmware, middleware, microcode, hardware description language, or any other name, software should be broadly interpreted as commands, command sets, code, code segments, program code, programs, subroutines, software modules, application programs, software applications, software packages, routines, subroutines, objects, executable files, execution threads, steps, functions, etc.

[0108] Furthermore, software, commands, information, etc., can be sent or received via a transmission medium. For example, when software is sent from a website, server, or other remote resource using wired technologies (coaxial cable, optical fiber, twisted pair, digital subscriber line (DSL), etc.) and / or wireless technologies (infrared, microwave, etc.), these wired and / or wireless technologies are included within the definition of transmission medium.

[0109] The terms “system” and “network” used in this manual are interchangeable.

[0110] In this manual, the terms "base station (BS)," "wireless base station," "eNB," "gNB," "cell," "sector," "cell group," "carrier," and "component carrier" are used interchangeably. Base stations are sometimes also referred to as fixed stations, NodeBs, eNodeBs (eNBs), access points, transmitting points, receiving points, femtocells, small cells, etc.

[0111] A base station can accommodate one or more (e.g., three) cells (also called sectors). When a base station accommodates multiple cells, the entire coverage area of ​​the base station can be divided into multiple smaller areas, each of which can also provide communication services through a base station subsystem (e.g., an indoor small cell (remote radio head (RRH))). Terms such as "cell" or "sector" refer to a portion or the entire coverage area of ​​the base station and / or base station subsystem that provides communication services within that coverage area.

[0112] In this specification, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" are used interchangeably. A mobile station is sometimes also referred to by those skilled in the art as a user station, mobile unit, user unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile user station, access terminal, mobile terminal, wireless terminal, remote terminal, handheld device, user agent, mobile client, client, or several other appropriate terms.

[0113] Furthermore, the wireless base station in this specification can also be replaced by a user equipment. For example, various methods / implementations of this disclosure can also be applied to a structure that replaces communication between the wireless base station and the user equipment with communication between multiple user equipment (D2D, Device-to-Device). In this case, the functions of the first or second communication device in the device 800 described above can be regarded as the functions of the user equipment. In addition, terms such as "uplink" and "downlink" can also be replaced with "side". For example, the uplink channel can also be replaced with the side channel.

[0114] Similarly, the user equipment described in this specification can be replaced by a wireless base station. In this case, the functions of the user equipment described above can be regarded as the functions of the first communication device or the second communication device.

[0115] In this specification, specific actions performed via a base station may sometimes also be performed via its upper node, depending on the circumstances. Clearly, in a network consisting of one or more network nodes with a base station, various actions performed for communication with a terminal can be performed via the base station, one or more network nodes other than the base station (considering, but not limited to, Mobility Management Entities (MMEs), Serving Gateways (S-GWs), etc.), or combinations thereof.

[0116] The various methods / implementations described in this specification can be used individually or in combination, and can be switched during execution. Furthermore, the processing steps, sequences, flowcharts, etc., of the various methods / implementations described in this specification can be rearranged as long as there are no contradictions. For example, regarding the methods described in this specification, various step units are given in an exemplary order, but the method is not limited to the specific order given.

[0117] The methods / implementations described in this specification can be applied to systems utilizing Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), Super 3G, IMT-Advanced, 4G, 5G, Future Radio Access (FRA), New-RAT (Radio Access Technology), New Radio (NR), New Radio Access (NX), Future Generation Radio Access (FX), Global System for Mobile Communications (GSM), CDMA3000, Ultra Mobile Broadband (UMB), IEEE 920.11 (Wi-Fi), and IEEE... Systems based on and / or extended from WiMAX (registered trademark), IEEE 920.16, Ultra-Wideband (UWB), Bluetooth (registered trademark), other suitable wireless communication methods, and / or next-generation systems based on them.

[0118] The use of the word "based on" in this specification, unless explicitly stated elsewhere, does not imply "based on only". In other words, the use of "based on" refers to both "based on only" and "based on at least".

[0119] Any reference to units using the names "first," "second," etc., as used in this specification is not intended to fully define the number or order of these units. These names may be used in this specification as a convenient method of distinguishing two or more units. Therefore, reference to a first unit and a second unit does not imply that only two units may be used, or that the first unit must take precedence over the second unit in some form.

[0120] The term "determining" as used in this specification sometimes encompasses a variety of actions. For example, "determining" can refer to actions such as calculating, computing, processing, deriving, investigating, looking up (e.g., searching in tables, databases, or other data structures), and ascertaining. Furthermore, "determining" can also refer to actions such as receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, and accessing (e.g., accessing data in memory). Additionally, "determining" can refer to actions such as resolving, selecting, choosing, establishing, and comparing. In other words, "determining" can encompass several actions.

[0121] As used in this specification, the terms "connected," "coupled," or any variations thereof refer to any direct or indirect connection or combination between two or more units, including situations where one or more intermediate units exist between two mutually "connected" or "coupled" units. The combination or connection between units can be physical, logical, or a combination of both. For example, "connected" can also be replaced by "accessed." In this specification, it can be understood that two units are "connected" or "coupled" by using one or more wires, cables, and / or printed electrical connections, and, as several non-limiting and non-exhaustive examples, by using electromagnetic energy with wavelengths in the radio frequency region, microwave region, and / or light (both visible and invisible light) region.

[0122] When the terms "including," "comprising," and variations thereof are used in this specification or claims, these terms are open-ended, just like the term "possess." Furthermore, the term "or" as used in this specification or claims is not an XOR expression.

[0123] The present disclosure has been described in detail above; however, it will be apparent to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered ways without departing from the spirit and scope defined by the claims. Therefore, the description herein is for illustrative purposes only and is not intended to be restrictive.

Claims

1. A user equipment, comprising: A receiving unit is configured to receive downlink transmission data, including pilot signals, from a base station, wherein the pilot signals are low-resolution reference signals; The encoding unit is used to encode the pilot signal into feedback channel state information; as well as A transmitting unit is configured to transmit the feedback channel state information to the base station, and for the base station to reconstruct the channel matrix of the base station based on the feedback channel state information; The coding unit is configured with a coding neural network, which includes only one fully connected layer for quantizing and compressing the pilot signal into a one-dimensional vector as the feedback channel state information.

2. The user equipment as claimed in claim 1, wherein, The pilot signal is a pilot signal whose frequency is controlled by the base station.

3. A base station, comprising: The transmitting unit is used to transmit downlink transmission data, including pilot signals, to the user equipment, wherein the pilot signals are low-resolution reference signals; A receiving unit is configured to receive uplink transmission data from a user equipment, the uplink transmission data including feedback channel state information generated by a coded neural network consisting of only one fully connected layer based on the pilot signal quantized and compressed into a one-dimensional vector; as well as A decoding unit is used to decode the feedback channel state information to obtain the channel matrix of the base station.

4. The base station as described in claim 3, wherein, The transmitting unit controls the frequency of the pilot signal.

5. The base station as described in claim 3 or 4, wherein, The decoding unit is configured with a decoding neural network, which includes at least a multi-layer residual convolutional neural network, for super-resolution reconstruction of the feedback channel state information into the channel matrix of the base station.

6. A joint training device for user equipment and base station, comprising: The receiving unit is used to receive pilot signals and training pilot signals from the base station, wherein the pilot signals from the base station are low-resolution reference signals; The training unit uses at least an encoding neural network to encode the pilot signal into feedback channel state information and at least a decoding neural network to decode the feedback channel state information to reconstruct the channel matrix of the base station. The encoding neural network includes only one fully connected layer for quantizing and compressing the pilot signal into a one-dimensional vector as the feedback channel state information. The training channel matrix is ​​obtained based on the training pilot signal, and the training unit constructs a loss function based on the channel matrix and the training channel matrix, jointly training the encoding neural network and the decoding neural network; and Output the parameters of the encoding neural network and the decoding neural network.

7. A joint channel estimation and feedback system including user equipment and base station, comprising: User equipment (UE) is configured to receive downlink transmission data including pilot signals from a base station, encode the pilot signals into feedback channel state information (CSS), and transmit the CSS to the base station. The pilot signals are low-resolution reference signals. The UE is configured with an encoding neural network comprising only one fully connected layer for quantizing and compressing the pilot signals into a one-dimensional vector as the CSS. The base station transmits downlink transmission data, including pilot signals, to the user equipment and receives uplink transmission data from the user equipment. The uplink transmission data includes feedback channel state information generated based on the quantization and compression of the pilot signals into a one-dimensional vector. The base station also decodes the feedback channel state information to obtain the channel matrix of the base station.

8. A method for generating feedback channel state information executed by a user equipment, comprising: Receive downlink transmission data from the base station, including pilot signals, where the pilot signals are low-resolution reference signals; The pilot signal is encoded into feedback channel state information; as well as The feedback channel state information is sent to the base station, so that the base station can reconstruct the channel matrix of the base station based on the feedback channel state information; Encoding the pilot signal into feedback channel state information includes: quantizing and compressing the pilot signal into a one-dimensional vector using a coding neural network consisting of only one fully connected layer, as the feedback channel state information.

9. A channel matrix generation method performed by a base station, comprising: Send downlink transmission data, including pilot signals, to user equipment, wherein the pilot signals are low-resolution reference signals; Uplink transmission data is received from the user equipment, the uplink transmission data including feedback channel state information generated by a coded neural network consisting of only one fully connected layer based on the pilot signal quantized and compressed into a one-dimensional vector; as well as The feedback channel state information is decoded to obtain the channel matrix of the base station.

10. A joint training method for user equipment and base station, comprising: Receive pilot signals and training pilot signals from the base station, wherein the pilot signals from the base station are low-resolution reference signals; The pilot signal is encoded into feedback channel state information using at least an encoding neural network, and the feedback channel state information is decoded using at least a decoding neural network to reconstruct the channel matrix of the base station. The pilot signal is quantized and compressed into a one-dimensional vector as the feedback channel state information using an encoding neural network comprising only one fully connected layer. The training channel matrix is ​​obtained based on the training pilot signal. A loss function is constructed based on the channel matrix and the training channel matrix, and the encoding neural network and the decoding neural network are jointly trained; and Output the parameters of the encoding neural network and the decoding neural network.

11. A joint channel estimation and feedback method for user equipment and base station, comprising: The base station sends downlink transmission data, including pilot signals, to the user equipment, wherein the pilot signals are low-resolution reference signals; The user equipment encodes the pilot signal into feedback channel state information and sends the feedback channel state information to the base station. The user equipment is configured with an encoding neural network, which includes only one fully connected layer for quantizing and compressing the pilot signal into a one-dimensional vector as the feedback channel state information. The base station receives uplink transmission data from the user equipment, the uplink transmission data including the feedback channel state information generated based on the pilot signal quantized and compressed into a one-dimensional vector; The base station decodes the feedback channel state information to obtain the base station's channel matrix.

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